<i>Qanuq ukua kanguit sunialiqpitigu?</i> (What should we do with all of these geese?) Collaborative research to support wildlife co-management and Inuit self-determination
Bibliographic record
Abstract
Inuit living in Nunavut have harvested light geese and lived near goose colonies for generations. Inuit knowledge includes important information about light goose ecology and management that can inform co-management and enhance scientific research and monitoring. Since the 1970s, populations of light geese (Snow and Ross’ Geese; kanguit and kangunnait in Inuktut; Chen caerulescens (Linnaeus, 1758) and Chen rossii (Cassin, 1861)) have experienced significant increases in abundance which led to habitat alteration in some portions of the central and eastern Canadian Arctic. In response to concerns expressed by Inuit and wildlife managers about light goose abundance, we conducted a collaborative research project in Arviat and Salliq (Coral Harbour), Nunavut, aiming to mobilize and document Inuit knowledge about light goose ecology and management in the Kivalliq region. Here, we explore the potential of collaborative research for mobilizing Inuit knowledge to support informed and inclusive decision-making about wildlife resources. First, we describe the participatory research methods employed to explore Inuit-identified management recommendations for light geese and engage co-management partners and research contributors to explore select management options. Then, we present these light goose management recommendations and options. Lastly, we discuss opportunities and challenges around the use of collaborative research to support wildlife co-management and Inuit self-determination. Inuit nunaqaqtut Nunavuumi angunasuksimalirmata kanguqpangnik kangurniglu nunaqarvingita sanianni araagunik unuqtunnik. Inuit qaujimaningat ilaqaqpuq aturnilingnik kanguit niqinginnik mianirijauninginniklu tusaumatitaulutik qaujisarningit mianiriyaunigillu. Taimangat 1970s atuqtilugit, kanguit unirningit (kanguit amma kanguaryuit Inuktut; Chen caerulescens (Linnaeus, 1758) amma Chen rossii (Cassin, 1861)) ayunganaqtukut pisimangmata unulialiqlutik amma niqiqatiarungnauqlutik Kanataup uqiuktaqtunngani. Tamana piblugu Inuit uumayuliriyillu isumaalulirmata kanguit unulualirninginnik, taima qaujisarnirmik pigialauqpugut Arvianni and Sallim (Coral Harbour), Nunavuumi, aulataulutik amma qaujisagaulutik Inuit kaujimajagit kangurnik Kivallirmi. Tavani atuqtuuluaqtunik qaujisarnirmut mianiqsinirmullu pitaqaqpuq Inuit nagminiq isumaliurlutik nirjutinut atugaksanullu. Sivullirmik, qaujisarniup qanuinninga isumagilugu kanguit mianirijauninginut. Amma suli, uqausirilirlugu kanguit mianirijauningat atugaujuuluaqtullu. Kingulirmik, uqausirilugu atuinnaujut amma ajurutaujut qaujisarniup iluanni nirjutinik amma Inuit nagminiq aulatuulualirninginnik.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".